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Citation: Ji, X.; Dong, F.; Zheng, C.;
Bu, N. The Influences of International
Trade on Sustainable Economic
Growth: An Economic Policy
Perspective. Sustainability 2022,14,
2781. https://doi.org/10.3390/
su14052781
Academic Editor: Bruce Morley
Received: 1 December 2021
Accepted: 23 February 2022
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sustainability
Article
The Influences of International Trade on Sustainable Economic
Growth: An Economic Policy Perspective
Xiuping Ji 1, Feiran Dong 2, *, Chen Zheng 3and Naipeng Bu 2
1Department of Economics, Business School, The University of Sussex, Brighton BN1 9RH, UK;
xj51@sussex.ac.uk
2Business School, Shandong University, Weihai 264209, China; bunp@sdu.edu.cn
3Business School, Edgehill University, Ormskirk L39 4QP, UK; chen.zheng@edgehill.ac.uk
*Correspondence: dongfeiran@sdu.edu.cn
Abstract:
This study uses the Gregory–Hansen cointegration method and the vector error correction
model in the vector autoregression system to reveal how international trade contributes to economic
sustainability. The Gregory–Hansen test for cointegration method reveals a permanent equilibrium
relation among sustainably economic growth, exports, and imports and shows that exports facilitate
GDP growth and accelerate improvements in the capability of imports in the long-run. The causality
between GDP and exports is unidirectional, indicating that exports area determinant of sustainable
economic growth. The bidirectional causality from imports to GDP also sheds light on the important
influence of imports on economic sustainability; however, GDP growth also drives import growth.
The interaction between imports and exports corresponds to their bidirectional causal relationship,
which is indicative of imports contributing to export production and of export growth expanding the
capacity for imports. This finding indicates that imports are both exogenous and endogenous factors
for exports.
Keywords: international trade; ECM model; sustainable economic growth
1. Introduction
Many previous studies have investigated how different variables contribute to eco-
nomic growth. Some drivers of economic growth mainly include government spending [
1
],
information and communication technologies (ICT) capital [
2
], human capital [
3
], inflation
and inflation uncertainty [
4
], and free trade [
5
]. An interesting topic exists regarding how
international trade affects economic growth;this topic would shed light on the relationship
between trade and sustainable economic growth, and policymakers can design effective
trade policies based on the empirical findings.
Based on the empirical study of [
6
], the results have shown that there is a strong
relationship between international trade and sustainable development, which is focused
on promoting prosperity and protecting the planet. At the global level, countries promote
two of the indicators contemplated in sustainable development: decent work, and eco-
nomic growth and poverty reduction, by integrating itself into the world economy with
other countries that follow the same goals around the world, as well as by increasingly
participating in the global value chain. The International Monetary Fund staff pointed
out that international trade has served as an impetus to enhance economic development,
and as a result, it has been highlighted that both developing and developed countries
have benefited from this economic prosperity, which has brought an increase in income
and standard of living, which has benefited the end of poverty. According to Frankel and
Romer [
7
] trade has positively affected income in a quantitatively large and robust way,
thus international trade improving standards of living. International trade contributes to
the acceleration of economic growth by allocating resources among different trading part-
ners and by specializing production in products that have relative comparative advantages.
Sustainability 2022,14, 2781. https://doi.org/10.3390/su14052781 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 2781 2 of 15
From the United Nations Conference on Trade and Development (UNCTAD) celebrated in
2006, many developing countries participated successfully in international trade, which
greatly helped these countries in attracting quantitatively large foreign direct investment
(FDI), providing them with a sustainable engine of economic development.
Since then, exports have turned the tables and have become a much more important
focus for economic growth in developing countries. The comparison between exports of
traditional basic products, exports of manufactures, and services have skyrocketed substan-
tially in developing countries since they joined the global value chain [
8
]. It is especially
evident in the case of the Asian giant, since the market-oriented reforms and the imple-
mentation of free trade have obtained higher incomes and achieved accelerated economic
growth, depending largely on exports. The aforementioned is demonstrated in the results
of the study of Chia and Elsevier [
9
] in which exports had a positive impact on economic
growth and that the export-oriented growth strategy was valid in selected countries in
sub-Saharan Africa; indeed, export expansion leads to a higher rate of economic growth.
The increasing intensity of global economic integration has introduced potential contribu-
tors to economic development, such as competition, learning and innovation, specialized
production, and economies of scale; therefore, most economists support export expansion
because of its potential economic benefits, especially economies of scale by specialization in
production. Due to small economic entities, some countries, such as developing ones, fail to
specialize in production without deep participation in international trade. Enterprises en-
gaging in exports are operating in relatively competitive sectors where employees earn the
highest wages and employers gain the highest profits. When participating in international
trade, some developing countries can access all possible sources of innovation, including
foreign direct investments (FDI) and technological transfer, and integrate them into their
investments and production. New techniques and technological innovations endogenously
bring about new production techniques, new products, and higher total factor productivity.
These strategies and innovations produce even more exports in an upward spiral cycle
in the economic growth model, and this outcome contributes to and even accelerates the
growth of a nation. Consequently, the supply of products beyond the domestic demand
leads to surplus supply and extra exports.
However, this theoretical link is driven by empirical studies on international trade
policies. The import-led growth strategy indicates that the dominant cause of economic
growth is growth in imports. This endogenous growth model implies that imports allow do-
mestic industries to obtain intermediate factors for intensive production and internationally
advanced technologies, such as imported machinery and growth-enhancing foreign R&D
knowledge from rich countries, leading to the higher growth of developing countries [
10
].
The export-led growth strategy considers export expansion and trade liberalization as
critical determinants of growth due to positive economic externalities. According to Bald-
win et al. [
11
], international trade creates a large world market; therefore, economies can
benefit from capturing international technology transfers and spillovers and from increas-
ing their profits from economies of scale and specialization, which would facilitate their
technological improvement and boost their productivity. International trade improves
not only resource allocation efficiency but also the diffusion of international technological
knowledge and development and technology transfers. The underlying driver of economic
and export growth may be an enhancement in productivity in tradable or non-tradable
industries. According to Yang [
12
], an exogenous increase in the volume of export trade
or improvement in the productivity of export sectors mainly drives economic growth,
export growth, and real exchange rate appreciation. This strategy is beneficial for several
developing countries, which expand their product markets wider than their domestic
consumer markets. Moreover, the foreign exchange obtained from exports enables the
importation of intermediate goods, hence contributing to capital formation and increasing
output. When developing countries involve themselves further in world markets, a large
volume of export trade triggers a massive influx of foreign exchange, which helps these
countries afford advanced technologies and capital inputs.
Sustainability 2022,14, 2781 3 of 15
However, excessive reliance on an export-led growth strategy may make a country
vulnerable to exogenous shocks, such as economic downturns in other areas. During the
global financial recession, the negative shocks stemming from the US drove developing
countries to adopt export-led growth strategies and suffer from economic downturns. For
instance, by adopting a low-cost and export-oriented strategy, the participation of China in
the global value chain has contributed to its rapid economic development since its reform
and opening-up in the late 1970s. With the potential negative shocks or economic threats in
mind, the Chinese government started to adopt a “dual circulation” strategy that focuses
on both internal and international circulations.
This paper contributes to a knowledge body, which illustrates the link between interna-
tional trade and sustainable economic growth. With the acceleration of strategic promotion
of Chinese exporting, the empirical evidence of the influences of participation in the global
market on Chinese economic growth should inspire some policymakers. Although there
are many studies on the relationship between international trade and economic growth
in the Chinese context, little is known about the impact on sustainable economic growth
through a lens of policy and some positive effects of economic policies which encourage
their foreign sectors in international trade and export expansion; furthermore, bidirectional
causality between international trade and GDP growth actually sustains the benefits that
China gains from policies, which promote key sectors of China’s economy; hence, this
paper fills the gap and makes the contributions of exports to productivity and sustainable
economic growth, as well as a virtuous cycle between GDP, exports, and imports.
2. Literature Review
Measuring economic growth is critical, and GDP level and GDP growth have sub-
stantial differences in their consequences. Economic growth mainly refers to growth in an
entity’s productive capacity in the long-term, which is typically measured by real growth
in GDP or the monetary value of all final goods and services produced within a country or
region in a given period. The size and economic performance of a country can be measured
by its GDP, while real GDP growth rate indicates the health of an economy. Feng [
13
]
investigated the effects of local economy on commercial real estate (CRE) performance and
found that GDP level, which represents the size of an economy, leads to the income return
and capital appreciation of CRE, whereas GDP growth, on behalf of economic growth,
leads to capital returns. Both the Office of Management and Budget and Congressional
Budget Office [
14
,
15
] indicate that economic growth contributes to significantly reducing
deficits by increasing revenue.
Previous studies have examined the nexus of both international trade policies and
economic prosperity. Continually implementing well-considered and effective export-
promoted and growth-oriented policies enables countries to reap the benefits of trade in
either the short or long-term, thus accelerating their development and leading to their
economic prosperity. Dilek and Aytac [
16
] pointed out that countries adopting export-
oriented strategies continuously absorb the inflow of foreign exchange, hence generating
more output and new employment opportunities and accumulating greater trade volume.
Chia and Elsevier [
9
] showed that exports significantly affected the economic growth of
sub-Saharan African countries from 1985 to 2014. Their panel cointegration estimation
results also indicated a long-term relationship between outputs and exports; meanwhile,
the statistical results of a nonlinear test indicated a significant bidirectional causality [17].
Different econometric techniques have been applied to study the effects of interna-
tional trade on economic growth. By applying both Granger causality tests and impulse
response functions to explore whether trade growth, including imports and export ex-
pansion, increases in economic development or vice versa, Awokuse [
18
] investigated the
causality among exports, imports, and GDP growth. Results from these methodologies in-
dicate that imports significantly promote economic growth, whereas GDP growth spurs the
growth, as seen in exports and imports in Argentina, Colombia, and Peru.Shandre and Gu-
lasekaran [
19
] explored nine essential Asian countries using a vector error correction model
Sustainability 2022,14, 2781 4 of 15
(VECM), and discovered that imports have a greater effect on output growth than exports.
Under the growth-led export assumption, the findings of their vector autoregression (VAR)
framework imply that the Granger causality among Canadian exports, terms of trade, and
GDP is statistically significant, and that export growth leads to GDP growth [
20
];therefore,
while cointegration analysis highlights long-term relations, the Granger causality test
determines the direction of causal relations among exports, imports, and growth.
3. Theory Background
3.1. International Trade Theory
Heckscher–Ohlin (H-O) theory explores and sheds light on the essence of the links
between GDP growth and growth in international trade. Specifically, this theory describes
the general equilibrium, which highlights the patterns of both production specialization
and trade exchange based on a country’s relative factor abundance and factor intensity
of production [
21
]. In other words, H–O theory posits that, in the absence of trade, a
developing country with abundant unskilled labor has a lower relative price of unskilled
intensive goods. As the theory holds, trade contributes to a convergence of products’
relative prices. In response to trade, the relative price of exported goods that are produced
by intensively unskilled labor tends to increase. Under the framework of H–O theory, the
Stolper–Samuelson (S–S) theorem, the factor–price equalization theorem, the Rybczynski
theorem, and the aggregate economic efficiency theorem give detailed explanations of the
effects of trade on growth. Specifically, S–S theorem indicates that with international trade,
a rise in the relative price of traded goods that intensively use a country’s abundant factor
increases the prices of such intensively used factors. Meanwhile, the factor–price equaliza-
tion theorem assumes that both countries involved in trade face the same commodity prices
and production techniques and produce the same two goods. This theorem posits that the
existence of international trade in commodities allows the prices of identical production
factors to be equalized across the involved countries. Meanwhile, when the relative goods
prices are controlled for, Rybczynski theorem posits that if the supply of an abundant factor
in a country increases, then the yield of the commodity that intensively uses the factor tends
to increase [
22
]. Aggregate economic efficiency theorem explains the changes in prices
resulting from specialization and trade exchange. These changes motivate each country
to generate more exports and fewer imports so that a greater welfare is attributable to the
improved production and consumption efficiency resulting from the changes in prices [
22
].
3.2. Unit Root Test, Cointegration, Vector Correction Model (VECM), and Granger Causality Test
(1) Unit root
Before estimating the dynamic relationship, the order of integration (i.e., the sta-
tionarity properties of an individual variable) of all variables must be identified. If a
non-stationary time series tend to have a unit root, then a spurious relationship among
these variables tends to be revealed in a regression analysis, hence leading to invalid causal-
ity. A unit root test should therefore be performed under the framework of regression
analysis. Based on the augmented Dickey–Fuller (ADF) test, the introduction of lagged
terms enables the variables to capture the omitted dynamics and eliminate the biased
standard errors. The ADF test needs to assume that the error process has equal statis-
tical variances; however, the asymptotic distribution of unit root test statistics remains
unchanged despite the heteroscedasticity. To test for a unit root or non-stationarity, the
ADF test is performed using Equation (1):
∆zt=u+γzt−1+βt+∑k
j=1Φj∆zt−j+et(1)
where
et
is an error term that is assumed to be stationary with a zero mean and a constant
variance. Under the null hypothesis of the existence of a unit root, McKinnoncritical values
are used for testing on the coefficients of zt−1.
Sustainability 2022,14, 2781 5 of 15
Zivot and Andrews [
23
] proposed an improved unit root test that considers structural
breaks. In this study, a structural break with an unknown break date is assumed. These
authors also noted that all locations of data are likely to be breakpoints, and all T statistics
that test
α=
1
α=ˆ
αA,ˆ
αBor ˆ
αC
are calculated through ADF cycle tests. The minimum
tvalue is chosen as the corresponding
λ
value (
λ=TB/T
,
TB
time of structural breakpoints),
where the estimated break date is obtained through
tˆ
aihˆ
λin f i=in f
λ∈Λtˆ
ai(λ)
. Given that the
null hypothesis of a unit root is
yt=µ+yt−1+et
, the regression equations used for testing
a unit root are listed as follows:
yt=ˆ
µA+ˆ
θADUtˆ
λ+ˆ
βAt+ˆ
αAyt−1+∑k
j=1ˆ
cA
j∆yt−j+ˆ
et, (2)
yt=ˆ
µB+ˆ
βBt+ˆ
γBDT∗
tˆ
λ+ˆ
αByt−1+∑k
j=1ˆ
cB
j∆yt−j+ˆ
et, (3)
yt=ˆ
µC+ˆ
θCDUtˆ
λ+ˆ
βCt+ˆ
γCDT∗
tˆ
λ+ˆ
αCyt−1+∑k
j=1ˆ
cC
j∆yt−j+ˆ
et, (4)
where
λ=TB/T
,
TB
is the time of structural breakpoints; when
t>Tλ
,
DUt(λ)=
1, and
DUt(λ)=
0 if otherwise; when
t>Tλ
,
DT∗
t(λ)=t−Tλ
and 0 if otherwise.
ˆ
λ
refers to the
estimated value at break fraction. The number of extra regressors k is the number of the
kth order lag, which is determined by the significance of t statistics. The criterion is that
t-statistic on
ˆ
ci
j(i=A,B,C)
is less than 1.6 in absolute value if
j>k
, whereasthe t-statistic
on
ˆ
ci
k(i=A,B,C)
is more than 1.6 when
j=k
. The critical value of asymptotic normal
distribution at the 10% significant level is 1.6.
For the traditional unit root test, the structural breakpoints tend to be ignored, which
leads to spurious regression; however, the Zivot–Andrews unit root test is used to test the
endogenous structural unit root, based on the null hypothesis of
yt
being a unit root. Three
different models exist:
Model A : ∆yt=ˆ
µA+ˆ
θADUtˆ
λ+ˆ
βAt+ˆ
αAyt−1+∑k
j=1ˆ
cA
j∆yt−j+ˆ
et, (5)
Model B : ∆yt=ˆ
µB+ˆ
βBt+ˆ
γBDT∗
tˆ
λ+ˆ
αByt−1+∑k
j=1ˆ
cB
j∆yt−j+ˆ
et, (6)
Model C : ∆yt=ˆ
µC+ˆ
θCDUtˆ
λ+ˆ
βCt+ˆ
γCDT∗
tˆ
λ+ˆ
αCyt−1+∑k
j=1ˆ
cC
j∆yt−j+ˆ
et(7)
In the empirical studies, Model C is assumed to be superior to Models A and B because
the majority of variables have an increasing trend over time. Their results are more robust.
(2) Cointegration test and VECM model
While assuming the causality between international trade and economic growth, a
cointegration test should be conducted prior the Granger causality analysis. The Engle-
Granger test evaluates the cointegration between two variables; however, for multiple
variable regression, the cointegrated relationship between trade and growth is checked
by Gregory–Hansen cointegration test when controlling for structural breaks. Granger’s
representation theorem posits that if multiple variables are cointegrated, then an error
correction mechanism (ECM) model that represents their dynamic connection exists. If the
first differences of two variables are stationary but their levels are non-stationary, then these
variables are cointegrated. Under a statistically established cointegration, the residuals
can be used to formulate the dynamic ECM, which is used to study the long- and short-
term causality between trade and GDP growth. According to Engle and Granger [
24
], to
prove the causality from trade to GDP growth, all coefficients of the lagged differences of
export and import growths are jointly significant. At the same time, the coefficient of the
one-period lagged error term from Equation (2) is statistically significant.
ECTt−1=θt−1=LnGDPt−1−αLnexportt−1−βLnim portt−1(8)
Sustainability 2022,14, 2781 6 of 15
Following cointegration theory, the VECM is specified with (p) lags; however, the
model is estimated with (p
−
1) lags. By computing the maximum likelihood estimates
in multivariate ECM, models can elaborate on howvariables respond to shocks when
temporarily deviating from long-term dynamics.
∆zt=∑p−1
i=1δiΠzt−i+Πzt−1+εt(9)
where
zt
is an (n
×
1) column vector of k variables,
µ
is an (n
×
1) vector of constant terms,
δ
and
Π
represent coefficient matrices,
zt−1
is the lagged error correction term (ECT),
∆
is
a difference operator, i denotes lag length, and
εtis the random error term
. The coefficient
matrix
Π
is known as the impact matrix that contains information about the long-term
relationships among the variables. Before using the Johansen VECM model, the order
of integration of the variables is tested. On the one hand, the VAR system can be used
when Rank (
Π
) =
r0=k
, which indicates that the full rank of the matrix
(Π)
has the
stationary vector process
zt
. On the other hand, the null matrix
Π
, which is designated
as Rank (
Π
) = 0, implies that the non-stationary
zt
is non-cointegrated; therefore, the VAR
portrayal of the involved variables can still be used only if these variables conduct the
first difference. Meanwhile, 0 < Rank (
Π
) < k suggests that
zt
series is non-stationary yet
cointegrated.The empirical implication of variables being cointegrated is that the involved
variable temporarily deviates from the long-run equilibrium due to shocks; however, these
variables stick to the long-run equilibrium throughout the entire period. In this study, two
maximum cointegrating equations exist consequently.
Πzt−1
contains information on the
term of
εt
derived from the equation, which leads to either a temporary difference from
the long-run equilibrium or the equilibrium state. The estimated coefficients of the lagged
variables
δi
can capture the fluctuations resulting from short-term shocks. The VECM based
on the VAR model focuses on the characteristics of time series and passes the diagnostic
tests. Under the framework of the time series, the estimated models must be free from
autoregression, while the residuals obtained from this estimation regression are supposed
to be stationary, follow a normal distribution, and have the same variance; therefore, the
VEC model is presented in Equations (10) in first differences:
∆ln GDPt=α1+
k−1
∑
i=1
δ1i∆ln GDPt−i+
k−1
∑
i=1
β1i∆ln ex portt−i+
k−1
∑
i=1
ϕ1i∆ln importt−i+λLGDP ECTt−1+υ1t(10)
where
k−
1 equals to lag length minus 1,
ϕi
,
βi
,
and δi
are the short-term coefficients, and
λiand ECTt−1
denote the speed of the adjustment parameter, which lies between 0 and 1,
and the ECT, which is the lagged value of the residuals from the estimation of cointegrated
regression, respectively.
However, with a break in any series, Gregory and Hansen [
25
] designed a test for
cointegration when controlling for structural breaks. The null hypothesis of the Gregory
and Hansen test is that no cointegration exists at the break point in an unknown date
against the alternative hypothesis of cointegration at the break point. This rejection of the
null hypothesis implies that the linear combination of variables shows the long-run stable
relationship. The following equations are the cointegration models:
Model 1 : Yt=α+γDUt+βXt+εt, (11)
Model 2 : Yt=α+γDUt+ηt+βXt+εt, (12)
Model 3 : Yt=α+γDUt+β1Xt+β2XtDUt+εt, (13)
when t>TB,DUt=1, and DUt=0 if otherwise. TBis the break date.
(3) Granger causality test
Granger [
26
] argued that the VECM model can be used if the variables have a coin-
tegrated relationship. The Granger causality test that follows the chi-distribution with p
degree of freedom is based on the null hypothesis that all estimated coefficients are 0 and on
Sustainability 2022,14, 2781 7 of 15
the alternative hypothesis of non-zero coefficients. Rejecting the null hypothesis suggests
the existence of unilaterally directional links from one variable to another. Specifically, the
null hypothesis (
HO
: the coefficient of an independent variable equals 0) highlights that the
independent variables fail to explain the changes in the dependent variable. For the joint
causality test, the variables fail to jointly explain the explained variable if they support
HO
(i.e., all coefficients are 0). In sum, the existence of a long-run link allows the identification
of one or more Granger causalities; however, the stably permanent relationship cannot
be inferred from the existence of a Granger causal relationship because of the short-term
Granger causality test.
4. Data and Methodology Historical Annual Data
Historical annual data on the real GDP, exports, and imports of China are obtained
from the World Bank national accounts data and the OECD National Accounts data files,
which cover the years 1986 to 2020. As known, Granger causality is a phenomenon that
requires a long period to be observed in order for complete interaction effects to take place
several years later; therefore, annual data are used instead of quarterly ones. The real GDP
growth (GDP; expressed in USD and calculated in 2010 base year) and the real exports
(EX) and real imports (IM; both of which are based on CPI 2010 = 100 and expressed in
USD) data are transformed into natural logarithm forms. Causality analysis is performed to
check whether exogenous events affect exports and imports than GDP. The shocks in trade
are inferred to have a higher likelihood of affecting the trade variables instead of GDP. The
fluctuations in imports are greater than those in exports. Table 1shows that three pairwise
variables have statistically significant and positively high correlation coefficients. These
high correlation coefficients that shed light on the pairwise variables are likely to also have
high linear correlation; however, the highly linear correlations between two variables do
not indicate that a causality must exist among real exports (Exports), real imports (Imports),
and real gross national product growth (GDP), which represents economic growth. In
other words, the model might be mis-specified. Multicollinearity also makes it difficult
to interpret of model and leads to an overfitting problem as well. When multicollinearity
occurs, change in one variable would result in change to another and there also exists a
significant fluctuation in the model results. The effective method of solving the correlated
variables is to transform the variables but still maintain variables’ feature; furthermore, we
need to conduct unit root test, cointegration test, and Granger causality Wald test must be
used to determine the direction and strength of the causality between international trade
and growth.
Table 1. Correlation Coefficient.
Variables ln GDP ln Export ln Import
ln GDP 1.000 0.948 0.994
ln export 1.000 0.952
ln import 1.000
5. Estimations and Analytical Results
The integrated order of variables and their cointegration must be checked. The
augmented Dickey–Fuller and Phillips–Perron tests are conducted to determine whether
a variable has a unit root under the null hypothesis that this variable includes a unit
root against the alternative hypothesis of a stationary variable. Neway and Kenneth [
27
]
standard errors are considered in the Phillips–Perron test to deal with the serial correlation.
The natural logarithm forms of all variable series present a unit root in both the ADF and
Phillips–Perron tests, as shown in Table 2. While the critical value of ADF at level is
−
2.620
at the significance level of 10%, which is beyond the rejection rejoins, the critical value of
ADF for the first difference is
−
2.662 at 10%. The null hypothesis, which posits that a unit
root is present, is accepted at level yet is rejected for the first difference, hence designating
those variables as being integrated of order 1. All variables are integrated of order 1, I
Sustainability 2022,14, 2781 8 of 15
(1), which is consistent with the results of the Phillips–Perron test. The Zivot–Andrews
unit root test is conducted because there exist breakpoints in time series. Conducting
the Gregory–Hansen test for cointegration because of the structural break, the optimum
lag lengths are decided according to the selection order criteria. The results of Gregory–
Hansen test indicate a cointegration; therefore, a long-term relationship is observed between
international trade (including exports and imports) and GDP. Despite short-term shocks
that lead to movements in the time series, these time series tend to converge in the long run.
Table 2. Unit root results fromthe augmented Dickey–Fuller testand Phillips–Perron test.
ADF test:
Variable Level First Difference Conclusion
ln GDP −1.287 −2.873 * I (1)
ln import −0.758 −3.726 *** I (1)
ln export −0.677 −3.144 ** I (1)
Phillip–Perron test:
Variable Level First Difference Conclusion
ln GDP −1.667 −2.626 * I (1)
ln import −0.416 −74.077 *** I (1)
ln export −0.865 −4.765 *** I (1)
* Significant at the 10% confidence level, ** significant at the 5% confidence level, and *** significant at the 1%
confidence level. According to the McKinnon critical values, the critical values of ADF are
−
2.620 in level and
−
2.662 in first difference at 10%, whereas the critical values of the Phillips–Perron test are
−
2.619 in level and
−2.620 in first difference at 10%.
Considering structural breaks, the Zivot–Andrews unit root test is conducted. In
Tables 3and 4, the Zivot–Andrews unit root test suggests that the null hypothesis of a unit
root is rejected at 5% for all variables, indicating that three time series have a fractional
stationary trend surrounding the breakpoints.
Table 3. Results of Zivot–Andrews model C.
Variable T TBk^
αt^
α
^
θt^
θ
^
γt^
γ
ln GDPC33 2015 2 0.597 6.346 0.052 * 3.070 −0.011 * −4.787
ln ex portC32 2003 1 0.653 5.688 0.39 * 3.11 −0.053 * −5.66
ln importC32 2003 1 0.7 8.494 −0.372 * −4.395 −0.046 * 4.317
* significant at the 10% confidence level. The superscript letter c refers to results of Zivot–Andrews test based on
Model C: ∆yt=ˆ
µC+ˆ
θCDUtˆ
λ+ˆ
βCt+ˆ
γCDT∗
tˆ
λ+ˆ
αCyt−1+∑k
j=1ˆ
cC
j∆yt−j+ˆ
et.
Table 4. Zivot–Andrews unit root test.
Variables
t-Statistic (A
Structural Break
in the Intercept)
t-Statistic (A
Structural Break
in the Trend)
t-Statistic (A
Structural Break in
the Intercept and
Trend)
Break Point
ln GDP −6.012 (2000) −6.072 (2015) −7.128 (2015) 2015
ln ex port −6.415 (2015) −6.311(2013) −6.331(2015) 2015
ln import −5.703(2002) −4.207(2012) −5.65(2002) 2002
The regression using 35 observations is more likely to cause bias and wrong infer-
ences if the breakpoint is ignored in the model. With a break in any series, Gregory and
Hansen [
25
] designed a test for cointegration when controlling for structural breaks. The
Sustainability 2022,14, 2781 9 of 15
Zt
values from Gregory–Hansen Models in Table 5suggest that a break is evident in 2015
when a reform of the foreign exchange rate system occurred; therefore, the results of the
Gregory–Hansen test for cointegration mean that a long-run relationship exists among
economic growth, export, and import, as shown in Table 5.
Table 5. Results from Gregory–Hansen Models.
Gregory–Hansen
Models
ADF ZtZa
Statistic Breakpoint Statisti Breakpoint Statistic Breakpoint
Intercept shift −6.02 ** 2013 −6.00 ** 2015 −34.67 2013
Intercept shift with trend
−7.13 ** 1998 −7.24 ** 1998 −44.09 1998
Intercept shift with slope
−5.39 2015 −5.43 ** 2015 −31.68 2015
** significant at the 5% confidence level.
Table 6shows that the coefficient for adjustment (
−
0.665) is significant at a 1% signifi-
cance level, and this rejection of the null hypothesis implies that the linear combination
of variables indicates a long-run stable relationship. It implies that international trade
contributes sustainably to economic growth in China. The long-term equilibrium link
among the variables is represented by the error correct term (ECT). The ECT shows how
much of the disequilibrium is being corrected. A negative coefficient indicates convergence,
while a positive coefficient indicates divergence. The adjustment term to the long-term
equilibrium is statistically significant at 1%, which suggests that the deviation from the
long-run equilibrium would be adjusted at convergence speeds of 0.665. If the lag value
of the GDP is currently below the equilibrium, then the reduction in GDP caused by a
decrease in exports and imports would allow the system to recover the equilibrium at the
speed of adjustment; therefore, a long-run relationship between economic growth and
growth in export and import exists.
Table 6. Results of the ECM model based on the ARDL model.
Variables lnGDPt−1lnExporttlnImporttDUtlnImportt*DUtlnExportt*DUt∆lnExportt∆lnImporttConstant
Coefficient −0.665 *** 0.710 *** −0.028 36.19 ** −0.472 −1.149 ** −0.099 −0.036 9.120 ***
−4.43 3.16 −0.13 2.60 −1.14 −2.057 −0.81 −0.32 4.06
***, **, and * indicate the rejection of the null hypothesis of 0 coefficients at the 1%, 5%, and 10% significance levels,
respectively.
Table 6shows that the breakpoint coefficient is statistically significant at 5%, implying
that breakpoints have a significant effect on this model. The coefficient of the dummy
variable
DUt
is 36.19, which is statistically significant at a 5% significant level, while cross-
terms between export, import, and dummy variable are
−
1.149 and
−
0.472. The remaining
coefficients relate to the short-run dynamics of the model’s convergence to equilibrium.
The coefficient is of 0.710 statistical significance at a 1% significance level, thereby implying
that growth in export leads to an economic boom in the short-run; however, the coefficient
is
−
0.028, which suggests the absence of a significant effect of import growth on economic
growth in the short-term.
The cumulative sums (CUSUM) of the recursive residuals are calculated to test for
model stability with the dummies in the model. The graph of the CUSUM of the recursive
residuals includes a 95% confidence band. The model is stable. Although the exact direc-
tions of causality are uncertain, the joint F-test provides information about the direction of
Granger causality. Engle and Granger [
24
] stated that if multiple variables are cointegrated,
then an ECM model must be developed, and the Granger causality can also be identified.
For further statistical analysis, the estimation is subjected to robust diagnostic tests to
identify the correct long-term relationship. The ECM model passes both normality and
heteroscedasticity tests. In this study, based on Autoregressive Distributed Lag (ARDL)
which is applicable for both non-stationary time series as well as for time series with a
mixed order of integration where the Granger causality test is performed.
Sustainability 2022,14, 2781 10 of 15
The main results of the Granger causality Wald tests are consistent almost with the
conclusions of the results of the ECM model. The causality between international trade and
growth should be checked to determine the causal directions. In this study, the
F
-statistics
from the Wald tests based on the ARDL model suggest the causal relation is robust in short-
run. All possible causality among the variables can be inferred according to the results of
the Granger causality Wald tests (Table 7). Given that China mainly relies on exports, a
unidirectional causality between GDP and exports is observed, suggesting that the growth
in exports leads to economic growth. Export expansion and promotion allow China to
expand its economy in several ways, such as by accumulating national foreign currency
earnings and attracting the inflow of foreign investment, enhancing its capacity utilization,
improving its technological progress and total factor productivity, making a much greater
use of economies of scale, generating more employment and increasing labor productivity,
and allocating scarce resources effectively across the economy. GDP also has positive effects
on imports, and imported machinery makes developing countries, especially emerging
ones, achieve industrialization. Table 7shows that exports and GDP jointly lead to imports.
Exports and imports jointly leading to GDP growth highlights the importance of import
inputs to export production and expansion. Evidently, international trade sustainably
contributes to economic growth, and economic prosperity enables international trade to
flourish in the world market.
Table 7. Results of the Granger causality Wald tests.
Null Hypothesis F Statistic for Granger
Causality Test Direction of Causality
Imports fail to cause GDP 9.308 *** Imports cause GDP
Exports fail to cause GDP 3.842 * Exports cause GDP
Imports and exports fail to cause GDP
7.799 *** Imports and exports jointly
cause GDP
GDP fails to cause imports 4.387 ** GDP cause imports
Exports fail to cause imports 21.5 *** Exports cause imports
GDP and exports fail to cause imports
23.245 ***
Exports and GDP jointly cause
imports
GDP fails to cause exports 0.246 GDP fails to cause exports
Imports fail to cause exports 24.358 *** Imports cause exports
GDP and imports fail to cause exports
11.958 *** Imports and GDP jointly
cause exports
* Indicates the null hypothesis of no causality is significant at the 10% confidence level, ** significant at the 5%
confidence level, and *** significant at the 1% confidence level.
In the impulse response analysis, all eigenvalues lie inside the unit circle, hence
implying that VAR satisfies the stability condition in Figure 1; however, one root approaches
the unit circle, which suggests that some shocks are persistent. The impulse responses of the
GDP, export, or import variables for forward 10 periods to the 1 standard deviation shock
observed in GDP, exports, or imports are then plotted. The impulse response functions
illustrate the behavior of variables with observed causality for each variable in China.
Sustainability 2022,14, 2781 11 of 15
Sustainability 2022, 14, 2781 11 of 15
shock observed in GDP, exports, or imports are then plotted. The impulse response func-
tions illustrate the behavior of variables with observed causality for each variable in
China.
Figure 1. Roots of companion matrix.
The forecast error variance decomposition and prediction of an 8-quarter forward
forecast reveal that 60.2%, 2.9%, and 36.9% of the forecast error variance are derived from
GDP, imports, and exports, respectively, as shown in Figure 2. In other words, GDP is
affected by itself, and exports contribute to economic growth. As illustrated in Figure 3,
the forecast error variance decomposition suggests that exports account for 9.24% of the
forecast error variance; meanwhile, GDP and imports significantly affect exports and ac-
count for 39.11% and 51.65% of the forecast error variance, respectively. Compared with
GDP (40.82%), exports (42.72%) contribute to a greater fluctuation in imports, as shown
in Figure 4.
Figure 2. GDP’s forecast error variance decomposition.
Figure 1. Roots of companion matrix.
The forecast error variance decomposition and prediction of an 8-quarter forward
forecast reveal that 60.2%, 2.9%, and 36.9% of the forecast error variance are derived from
GDP, imports, and exports, respectively, as shown in Figure 2. In other words, GDP is
affected by itself, and exports contribute to economic growth. As illustrated in Figure 3,
the forecast error variance decomposition suggests that exports account for 9.24% of the
forecast error variance; meanwhile, GDP and imports significantly affect exports and
account for 39.11% and 51.65% of the forecast error variance, respectively. Compared with
GDP (40.82%), exports (42.72%) contribute to a greater fluctuation in imports, as shown in
Figure 4.
Sustainability 2022, 14, 2781 11 of 15
shock observed in GDP, exports, or imports are then plotted. The impulse response func-
tions illustrate the behavior of variables with observed causality for each variable in
China.
Figure 1. Roots of companion matrix.
The forecast error variance decomposition and prediction of an 8-quarter forward
forecast reveal that 60.2%, 2.9%, and 36.9% of the forecast error variance are derived from
GDP, imports, and exports, respectively, as shown in Figure 2. In other words, GDP is
affected by itself, and exports contribute to economic growth. As illustrated in Figure 3,
the forecast error variance decomposition suggests that exports account for 9.24% of the
forecast error variance; meanwhile, GDP and imports significantly affect exports and ac-
count for 39.11% and 51.65% of the forecast error variance, respectively. Compared with
GDP (40.82%), exports (42.72%) contribute to a greater fluctuation in imports, as shown
in Figure 4.
Figure 2. GDP’s forecast error variance decomposition.
Figure 2. GDP’s forecast error variance decomposition.
The implication of the impulse response functions is supported by the results of the
VAR model and the Granger causality tests. When statistically significant impulse response
functions are considered, the exports of China generate a positive response to shocks, and
such impact turns into a negative response in the following periods. The impulse response
function behavior of GDP to the change in the exports of China is positive in the first period
and demonstrates a positive persistent trend.
Sustainability 2022,14, 2781 12 of 15
Sustainability 2022, 14, 2781 12 of 15
Figure 3. Export’s forecast error variance decomposition.
Figure 4. Import’s forecast error variance decomposition.
The implication of the impulse response functions is supported by the results of the
VAR model and the Granger causality tests. When statistically significant impulse re-
sponse functions are considered, the exports of China generate a positive response to
shocks, and such impact turns into a negative response in the following periods. The im-
pulse response function behavior of GDP to the change in the exports of China is positive
in the first period and demonstrates a positive persistent trend.
6. Conclusions and Implication
We can infer that both GDP growth and increase in imports are driven by exports in
China in the long-term. The test results reveal that exports facilitate GDP growth and that
exports accelerate growth in the capability of imports in the long-run. The Granger cau-
sality Wald tests reveal short-run relationships among the variables as follows. First, the
causality between GDP and exports is unidirectional, hence suggesting that exports area
determinant of economic growth. Second, GDP growth fails to contribute to exporting
production in the short-term. Third, the bidirectional causality from imports to GDP sheds
light on the important influence of imports on economic prosperity. Fourth, the interac-
tion between imports and exports corresponds to their bidirectional causal relationship.
Figure 3. Export’s forecast error variance decomposition.
Sustainability 2022, 14, 2781 12 of 15
Figure 3. Export’s forecast error variance decomposition.
Figure 4. Import’s forecast error variance decomposition.
The implication of the impulse response functions is supported by the results of the
VAR model and the Granger causality tests. When statistically significant impulse re-
sponse functions are considered, the exports of China generate a positive response to
shocks, and such impact turns into a negative response in the following periods. The im-
pulse response function behavior of GDP to the change in the exports of China is positive
in the first period and demonstrates a positive persistent trend.
6. Conclusions and Implication
We can infer that both GDP growth and increase in imports are driven by exports in
China in the long-term. The test results reveal that exports facilitate GDP growth and that
exports accelerate growth in the capability of imports in the long-run. The Granger cau-
sality Wald tests reveal short-run relationships among the variables as follows. First, the
causality between GDP and exports is unidirectional, hence suggesting that exports area
determinant of economic growth. Second, GDP growth fails to contribute to exporting
production in the short-term. Third, the bidirectional causality from imports to GDP sheds
light on the important influence of imports on economic prosperity. Fourth, the interac-
tion between imports and exports corresponds to their bidirectional causal relationship.
Figure 4. Import’s forecast error variance decomposition.
6. Conclusions and Implication
We can infer that both GDP growth and increase in imports are driven by exports
in China in the long-term. The test results reveal that exports facilitate GDP growth and
that exports accelerate growth in the capability of imports in the long-run. The Granger
causality Wald tests reveal short-run relationships among the variables as follows. First,
the causality between GDP and exports is unidirectional, hence suggesting that exports
area determinant of economic growth. Second, GDP growth fails to contribute to exporting
production in the short-term. Third, the bidirectional causality from imports to GDP sheds
light on the important influence of imports on economic prosperity. Fourth, the interaction
between imports and exports corresponds to their bidirectional causal relationship. Fifth,
the aforementioned variables demonstrate a joint causal relation from any two variates to
the remaining variate, underscoring the importance of the interplay among variable pairs
in the growth of the remaining variable.
The Gregory–Hansen test for cointegration method reveals a permanent equilibrium
relation among GDP, exports, and imports. Empirical results suggest that export growth
accelerates not only long-term economic growth but also the sustainable capacity to import
in China. The export-oriented policy has statistically significant and positive effects on
GDP growth, which is consistent with the conclusion of Emilio [
28
], who found that exports
significantly influence economic growth in developing countries. The movement patterns
Sustainability 2022,14, 2781 13 of 15
of output, either in the short- or long-term can be explained by exports. On the basis
of the analysis results, policymakers should continue its enforcement of the export-led
growth policy due to its contributions to economic growth; however, this strategy also
brings external shocks to a country, hence challenging policymakers who are enthusiastic
about export-led growth policies. In the recent global crisis triggered by the COVID-
19 pandemic, those countries across the world who have imposed lockdown policies
to curb the spread of virus are now facing an international trade recession. This crisis
has exposed the vulnerabilities of those countries that excessively rely on exports as a
crucial channel for GDP growth; therefore, at the onset of the recent global economic crisis,
policymakers should implement effective measures to buffer economies against negative
exogenous shocks, such as by operating prudent countercyclical fiscal deficits, achieving
a constrained fiscal expansion aimed at productive public spending, using large stocks
of foreign exchange reserves to finance the economy [
29
], aiming for a deeper and wider
regional integration, and promoting a greater trade-partner diversification [
30
]. These
effective and efficient measures used during global crises provide policymakers with some
approaches for mitigating the possible effects of negative shocks to GDP growth.
The estimated equations have important policy implications. The first cointegration
equation imposed on normalization restrictions highlights a relationship between exports
and GDP in the long-run. Export expansion contributes to imports because the foreign
exchange reserves used to pay for Chinese imports continue to accumulate. As a critical
determinant of growth, export expansion affects GDP growth through positive economic
externalities. At the same time, GDP has a prominent role in export expansion, and growth-
promoting exports allow a depreciation in the real exchange rate; therefore, the cheaper
prices of goods and services triggered by currency depreciation lead to further export
expansion. GDP is significantly affected by imports at the 1% level. Chinese imports
enable domestic industries to obtain advanced technologies, tools, and machineries from
developed countries. Economies can benefit from capturing international technology trans-
fers and spillovers and increasing their profits from economies of scale and specialization,
thereby facilitating their technological improvements and boosting their productivity. All
these points are supported by the results of the VEC model; therefore, China, being involved
in the global economy, relies heavily on the rapid economic growth of traded countries that
generate demand for exports. Meanwhile, the long-term relationship between exports and
growth suggests that China is dependent on export-led growth. The bidirectional causal
relationship between imports and economic growth from Granger causality Wald tests indi-
cates that economic growth promotes an increase in imports, and that imports contribute to
economic growth. Imports allow for high productivity, capital formation and accumulation
and economic growth due to technology and innovation transfer via the import channel;
therefore, it is necessary for policymakers to design import-oriented policies, including
lifting a range of restrictions and the less restrict Import Licensing Regulation.
Our findings related to the causal directions among exports, imports, and GDP offer
some policy implications for international trade. On the basis of the empirical results, China
should continue highlighting the importance of exports in its sustainable economic growth;
indeed, China was the second economic entity in the past to economically isolate itself
from other countries. Policymakers should continuously exploit the implications of the
export expansion strategy for China’s economic prospects, although countries that heavily
rely on export as a vital channel for sustainably economic growth are vulnerable, especially
during the global crisis and COVID-19 pandemic. There are some effective buffers against
negative external shocks, such as application of countercyclical policies, deeper and more
strengthened regional integration and wider diversification of trade partners, including the
Regional Comprehensive Economic Partnership (RCEP). Economic growth allows for an
export expansion, and the results of all models indicate that GDP is robustly associated
with positive exports; therefore, domestic and trade reforms, including reducing trade
tariffs, attracting foreign direct investment, and imposing constraints on imports, should
be implemented. These reforms may also benefit economic growth. Trade is an engine
Sustainability 2022,14, 2781 14 of 15
of economic growth, and domestic demand and consumption effectively drive economic
prosperity; therefore, in the face of potentially negative shocks or economic threats, the
Chinese government has adopted the “dual circulation” strategy, which focuses on both
internal and international circulation. That is, China implements the new dual circulation
strategy containing both “external circulation” which refers to access to global demand as
well as foreign capital and technology and “internal circulation” which means stocking
domestic demand and domestically developed technology.
Author Contributions:
Writing—original draft, X.J.; Data curation, X.J.; Formal analysis, X.J.; Soft-
ware, X.J. and N.B.; Writing—review & editing, F.D., N.B.; Supervision, F.D.; Project administration,
F.D.; Investigation, C.Z. and N.B.; Resources, C.Z. and N.B.; Validation, C.Z. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
We would like to thank the two anonymous reviewers, the editor, and the
Haoyue Yu, Liyuan Kong, Yuanhua Yu, and Yanzhen Yu for their helpful comments and support on
the early versions of this manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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